Isolated hand pose estimation. RGB-based single (isolated) hand pose estimation has made significant progress in the past few years. Zimmermann et al.  [1]} introduced one of the first deep learning models to estimate hand poses from monocular RGB images. It first uses HandSegNet to localize hand regions, then uses PoseNet to estimate 2D hand poses, and finally maps 2D poses into 3D space. Iqbal et al.  [2]} proposed to encode hand joint locations with 2.5D heatmap representation to address the depth ambiguity problems and improve localization precision. Spurr et al.  [3]} proposed a VAE-based generative model to regress 3D hand joint locations. Zhou et al.  [4]} proposed to fully exploit non-image MoCap data to improve model generalization and robustness. Recently, many works also attempt to estimate 3D hand meshes from monocular RGB images. Most of them [5]}, [6]}, [7]} are model-based, which train a convolutional neural network to estimate the MANO parameters [8]}. Others are model-free, which directly regress 3D vertices of the human hand using mesh convolution [9]}, graph neural networks [10]}, or transformers [11]}.
